Machine learning for cyber physical systems [electronic resource] : selected papers from the International Conference ML4CPS 2015 / Oliver Niggemann, Jürgen Beyerer (eds.).
ML4CPS (Conference) (2015 : Lemgo, Germany); Niggemann, Oliver, editor.; Beyerer, Jürgen, editor.
2016
Q325.5
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Title Machine learning for cyber physical systems [electronic resource] : selected papers from the International Conference ML4CPS 2015 / Oliver Niggemann, Jürgen Beyerer (eds.).
Meeting Name ML4CPS (Conference) (2015 : Lemgo, Germany)
ISBN 9783662488386 (electronic book)
3662488388 (electronic book)
9783662488362
DOI https://doi.org/10.1007/978-3-662-48838-6
Published Berlin : Springer Vieweg, 2016.
Language English
Description 1 online resource (vi, 121 pages) : illustrations.
Item Number 10.1007/978-3-662-48838-6 doi
Call Number Q325.5
Dewey Decimal Classification 006.3/1
Summary The work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS - Machine Learning for Cyber Physical Systems, which was held in Lemgo, October 1-2, 2015. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed February 23, 2016).
Added Author Niggemann, Oliver, editor.
Beyerer, Jürgen, editor.
Series Technologien für die intelligente Automation.
Available in Other Form Machine Learning for Cyber Physical Systems : Selected papers from the International Conference ML4CPS 2015
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Development of a Cyber-Physical System based on selective dynamic Gaussian naive Bayes model for a self-predict laser surface heat treatment process control
Evidence Grid Based Information Fusion for Semantic Classifiers in Dynamic Sensor Networks
Forecasting Cellular Connectivity for Cyber- Physical Systems: A Machine Learning Approach
Towards Optimized Machine Operations by Cloud Integrated Condition Estimation
Prognostics Health Management System based on Hybrid Model to Predict Failures of a Planetary Gear Transmission
Evaluation of Model-Based Condition Monitoring Systems in Industrial Application Cases
Towards a novel learning assistant for networked automation systems
Effcient Image Processing System for an Industrial Machine Learning Task
Efficient engineering in special purpose machinery through automated control code synthesis based on a functional categorisation
Geo-Distributed Analytics for the Internet of Things
Imple mentation and Comparison of Cluster-Based PSO Extensions in Hybrid Settings with Efficient Approximation
Machine-specifc Approach for Automatic Classifcation of Cutting Process Efficiency
Meta-analysis of Maintenance Knowledge Assets Towards Predictive Cost Controlling of Cyber Physical Production Systems
Towards Autonomously Navigating and Cooperating Vehicles in Cyber-Physical Production Systems.

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